In [2]:
!pip install yfinance==0.2.4
#!pip install pandas==1.3.3

#!pip install pandas==1.3.3
#!pip install requests==2.26.0
!mamba install bs4==4.10.0 -y
!mamba install html5lib==1.1 -y 
!pip install lxml==4.6.4
#!pip install plotly==5.3.1

!pip install yfinance==0.1.67
!mamba install bs4==4.10.0 -y
!pip install nbformat==4.2.0
Collecting yfinance==0.2.4
  Downloading yfinance-0.2.4-py2.py3-none-any.whl (51 kB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 51.4/51.4 kB 9.9 MB/s eta 0:00:00
Requirement already satisfied: pandas>=1.3.0 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from yfinance==0.2.4) (1.3.5)
Requirement already satisfied: numpy>=1.16.5 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from yfinance==0.2.4) (1.21.6)
Requirement already satisfied: requests>=2.26 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from yfinance==0.2.4) (2.29.0)
Collecting multitasking>=0.0.7 (from yfinance==0.2.4)
  Downloading multitasking-0.0.11-py3-none-any.whl (8.5 kB)
Requirement already satisfied: lxml>=4.9.1 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from yfinance==0.2.4) (4.9.2)
Collecting appdirs>=1.4.4 (from yfinance==0.2.4)
  Downloading appdirs-1.4.4-py2.py3-none-any.whl (9.6 kB)
Requirement already satisfied: pytz>=2022.5 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from yfinance==0.2.4) (2023.3)
Collecting frozendict>=2.3.4 (from yfinance==0.2.4)
  Downloading frozendict-2.4.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (103 kB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 103.1/103.1 kB 19.0 MB/s eta 0:00:00
Requirement already satisfied: cryptography>=3.3.2 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from yfinance==0.2.4) (38.0.2)
Requirement already satisfied: beautifulsoup4>=4.11.1 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from yfinance==0.2.4) (4.11.1)
Collecting html5lib>=1.1 (from yfinance==0.2.4)
  Downloading html5lib-1.1-py2.py3-none-any.whl (112 kB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 112.2/112.2 kB 25.0 MB/s eta 0:00:00
Requirement already satisfied: soupsieve>1.2 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from beautifulsoup4>=4.11.1->yfinance==0.2.4) (2.3.2.post1)
Requirement already satisfied: cffi>=1.12 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from cryptography>=3.3.2->yfinance==0.2.4) (1.15.1)
Requirement already satisfied: six>=1.9 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from html5lib>=1.1->yfinance==0.2.4) (1.16.0)
Requirement already satisfied: webencodings in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from html5lib>=1.1->yfinance==0.2.4) (0.5.1)
Requirement already satisfied: python-dateutil>=2.7.3 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from pandas>=1.3.0->yfinance==0.2.4) (2.8.2)
Requirement already satisfied: charset-normalizer<4,>=2 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from requests>=2.26->yfinance==0.2.4) (3.1.0)
Requirement already satisfied: idna<4,>=2.5 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from requests>=2.26->yfinance==0.2.4) (3.4)
Requirement already satisfied: urllib3<1.27,>=1.21.1 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from requests>=2.26->yfinance==0.2.4) (1.26.15)
Requirement already satisfied: certifi>=2017.4.17 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from requests>=2.26->yfinance==0.2.4) (2023.5.7)
Requirement already satisfied: pycparser in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from cffi>=1.12->cryptography>=3.3.2->yfinance==0.2.4) (2.21)
Installing collected packages: multitasking, appdirs, html5lib, frozendict, yfinance
Successfully installed appdirs-1.4.4 frozendict-2.4.0 html5lib-1.1 multitasking-0.0.11 yfinance-0.2.4

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Looking for: ['bs4==4.10.0']

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pkgs/r/linux-64    ━━━━━━━━━━━━╸━━━━━━━━━━━━   0.0 B /  ??.?MB @  ??.?MB/s  0.1s
pkgs/r/noarch      ━━━━━━━╸━━━━━━━━━━━━━━━╸━   0.0 B /  ??.?MB @  ??.?MB/s  0.1s[+] 0.2s
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pkgs/main/noarch   ━━━━━━━━━━━━╸━━━━━━━━━━━━  28.7kB /  ??.?MB @ 184.6kB/s  0.2s
pkgs/r/linux-64    ━━━━━━━━━━━━━━╸━━━━━━━━━━   0.0 B /  ??.?MB @  ??.?MB/s  0.2s
pkgs/r/noarch      ━━━━━━━━━━╸━━━━━━━━━━━━━━  12.3kB /  ??.?MB @  79.3kB/s  0.2s[+] 0.3s
pkgs/main/linux-64 ━━━━━━━╸━━━━━━━━━━━━━━━╸━ 413.7kB /  ??.?MB @   1.6MB/s  0.3s
pkgs/main/noarch   ━━━━━━━━━━━━━━╸━━━━━━━━━━ 581.6kB /  ??.?MB @   2.3MB/s  0.3s
pkgs/r/linux-64    ━━━━━━━━╸━━━━━━━━━━━━━━━━ 507.9kB /  ??.?MB @   2.0MB/s  0.3s
pkgs/r/noarch      ━━━━━━━━━━━━╸━━━━━━━━━━━━ 475.1kB /  ??.?MB @   1.8MB/s  0.3spkgs/main/noarch                                   861.6kB @   2.7MB/s  0.4s
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pkgs/main/linux-64 ━━━━━━━━━╸━━━━━━━━━━━━━━━   1.0MB /  ??.?MB @   2.6MB/s  0.4s
pkgs/r/linux-64    ━━━━━━━━━━╸━━━━━━━━━━━━━━ 745.5kB /  ??.?MB @   2.4MB/s  0.4s
pkgs/r/noarch      ━━━━━━━━━━━━━╸━━━━━━━━━━━ 729.1kB /  ??.?MB @   2.4MB/s  0.4s[+] 0.5s
pkgs/main/linux-64 ━━━━━━━━━━━╸━━━━━━━━━━━━━   1.5MB /  ??.?MB @   3.1MB/s  0.5s
pkgs/r/linux-64    ━━━━━━━━━━━━╸━━━━━━━━━━━━   1.4MB /  ??.?MB @   3.1MB/s  0.5s
pkgs/r/noarch      ━━━━━━━━╸━━━━━━━━━━━━━━━━   1.4MB /  ??.?MB @   3.1MB/s  0.5s[+] 0.6s
pkgs/main/linux-64 ━━━━━━━━━━━━━╸━━━━━━━━━━━   1.8MB /  ??.?MB @   3.3MB/s  0.6s
pkgs/r/noarch      ━━━━━━━━━╸━━━━━━━━━━━━━━━   1.7MB /  ??.?MB @   3.2MB/s  0.6spkgs/r/linux-64                                      1.9MB @   3.4MB/s  0.6s
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pkgs/main/linux-64 ━━━━━━━━━━━━━╸━━━━━━━━━━   2.2MB @   3.5MB/s             0.7s
pkgs/r/noarch      ━━━━━━━━━━━━━━━━━━━━━━━━   2.3MB @   3.5MB/s Finalizing  0.7spkgs/r/noarch                                      @   3.5MB/s  0.7s
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pkgs/main/linux-64 ━━━━━━━╸━━━━━━━━━━━━━━━━━   2.6MB /  ??.?MB @   3.5MB/s  0.8s[+] 0.9s
pkgs/main/linux-64 ━━━━━━━━━╸━━━━━━━━━━━━━━━   3.2MB /  ??.?MB @   3.7MB/s  0.9s[+] 1.0s
pkgs/main/linux-64 ━━━━━━━━━━━━╸━━━━━━━━━━━━   3.6MB /  ??.?MB @   3.7MB/s  1.0s[+] 1.1s
pkgs/main/linux-64 ━━━━━━━━━━━━━━╸━━━━━━━━━━   4.1MB /  ??.?MB @   3.8MB/s  1.1s[+] 1.2s
pkgs/main/linux-64 ━╸━━━━━━━━━━━━━━━╸━━━━━━━   4.6MB /  ??.?MB @   3.9MB/s  1.2s[+] 1.3s
pkgs/main/linux-64 ━━━╸━━━━━━━━━━━━━━━╸━━━━━   5.1MB /  ??.?MB @   4.0MB/s  1.3s[+] 1.4s
pkgs/main/linux-64 ━━━━━╸━━━━━━━━━━━━━━━╸━━━   5.7MB /  ??.?MB @   4.1MB/s  1.4s[+] 1.5s
pkgs/main/linux-64 ━━━━━━━━╸━━━━━━━━━━━━━━━━   6.1MB /  ??.?MB @   4.2MB/s  1.5s[+] 1.6s
pkgs/main/linux-64 ━━━━━━━━━━╸━━━━━━━━━━━━━━   6.7MB /  ??.?MB @   4.2MB/s  1.6s[+] 1.7s
pkgs/main/linux-64 ━━━━━━━━━━━━━━━━━━━━━━━━   6.8MB @   4.2MB/s Finalizing  1.7spkgs/main/linux-64                                 @   4.2MB/s  1.7s

Pinned packages:
  - python 3.7.*


Transaction

  Prefix: /home/jupyterlab/conda/envs/python

  Updating specs:

   - bs4==4.10.0
   - ca-certificates
   - certifi
   - openssl


  Package              Version  Build         Channel                 Size
────────────────────────────────────────────────────────────────────────────
  Install:
────────────────────────────────────────────────────────────────────────────

  + bs4                 4.10.0  hd3eb1b0_0    pkgs/main/noarch        10kB

  Upgrade:
────────────────────────────────────────────────────────────────────────────

  - ca-certificates   2023.5.7  hbcca054_0    conda-forge                 
  + ca-certificates  2024.3.11  h06a4308_0    pkgs/main/linux-64     130kB
  - openssl             1.1.1t  h0b41bf4_0    conda-forge                 
  + openssl             1.1.1w  h7f8727e_0    pkgs/main/linux-64       4MB

  Downgrade:
────────────────────────────────────────────────────────────────────────────

  - beautifulsoup4      4.11.1  pyha770c72_0  conda-forge                 
  + beautifulsoup4      4.10.0  pyh06a4308_0  pkgs/main/noarch        87kB

  Summary:

  Install: 1 packages
  Upgrade: 2 packages
  Downgrade: 1 packages

  Total download: 4MB

────────────────────────────────────────────────────────────────────────────


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Downloading  (2) ━━━━━━━━━━━━━━━━━━━━━━━   0.0 B beautifulsoup4             0.0s
Extracting       ━━━━━━━━━━━━━━━━━━━━━━━       0                            0.0s[+] 0.1s
Downloading  (4) ━━━━━━━━━━━━━━━━━━━━━━━   0.0 B beautifulsoup4             0.1s
Extracting       ━━━━━━━━━━━━━━━━━━━━━━━       0                            0.0sca-certificates                                    130.4kB @ 932.6kB/s  0.1s
beautifulsoup4                                      86.6kB @ 581.0kB/s  0.2s
bs4                                                 10.2kB @  68.6kB/s  0.2s
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Downloading  (1) ━━━━━━━━━━━━━━━━━━━━━━━   4.1MB openssl                    0.2s
Extracting   (3) ━━━━━━━━━━╸━━━━━━━━━━━━       0 beautifulsoup4             0.0sopenssl                                              3.9MB @  19.6MB/s  0.2s
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Downloading      ━━━━━━━━━━━━━━━━━━━━━━━   4.1MB                            0.3s
Extracting   (4) ━━━━━━━━━━━╸━━━━━━━━━━━       0 beautifulsoup4             0.1s[+] 0.4s
Downloading      ━━━━━━━━━━━━━━━━━━━━━━━   4.1MB                            0.3s
Extracting   (4) ━━━━━━━━━━━━╸━━━━━━━━━━       0 beautifulsoup4             0.2s[+] 0.5s
Downloading      ━━━━━━━━━━━━━━━━━━━━━━━   4.1MB                            0.3s
Extracting   (4) ━━━━━━━━━━━━━╸━━━━━━━━━       0 beautifulsoup4             0.3s[+] 0.6s
Downloading      ━━━━━━━━━━━━━━━━━━━━━━━   4.1MB                            0.3s
Extracting   (4) ━━━━━━━━━━━━━━╸━━━━━━━━       0 bs4                        0.4s[+] 0.7s
Downloading      ━━━━━━━━━━━━━━━━━━━━━━━   4.1MB                            0.3s
Extracting   (4) ━━━━━━━━━━━━━━━╸━━━━━━━       0 bs4                        0.5s[+] 0.8s
Downloading      ━━━━━━━━━━━━━━━━━━━━━━━   4.1MB                            0.3s
Extracting   (4) ╸━━━━━━━━━━━━━━━╸━━━━━━       0 bs4                        0.6s[+] 0.9s
Downloading      ━━━━━━━━━━━━━━━━━━━━━━━   4.1MB                            0.3s
Extracting   (4) ━╸━━━━━━━━━━━━━━━╸━━━━━       0 bs4                        0.7s[+] 1.0s
Downloading      ━━━━━━━━━━━━━━━━━━━━━━━   4.1MB                            0.3s
Extracting   (4) ━━╸━━━━━━━━━━━━━━━╸━━━━       0 ca-certificates            0.8s[+] 1.1s
Downloading      ━━━━━━━━━━━━━━━━━━━━━━━   4.1MB                            0.3s
Extracting   (4) ━━━━╸━━━━━━━━━━━━━━━╸━━       0 ca-certificates            0.9s[+] 1.2s
Downloading      ━━━━━━━━━━━━━━━━━━━━━━━   4.1MB                            0.3s
Extracting   (3) ━━━━╸━━━━━━━━━━━━━━━━━━       1 beautifulsoup4             1.0s[+] 1.3s
Downloading      ━━━━━━━━━━━━━━━━━━━━━━━   4.1MB                            0.3s
Extracting   (3) ━━━━╸━━━━━━━━━━━━━━━━━━       1 beautifulsoup4             1.1s[+] 1.4s
Downloading      ━━━━━━━━━━━━━━━━━━━━━━━   4.1MB                            0.3s
Extracting   (3) ━━━━╸━━━━━━━━━━━━━━━━━━       1 beautifulsoup4             1.2s[+] 1.5s
Downloading      ━━━━━━━━━━━━━━━━━━━━━━━   4.1MB                            0.3s
Extracting   (3) ━━━━╸━━━━━━━━━━━━━━━━━━       1 beautifulsoup4             1.3s[+] 1.6s
Downloading      ━━━━━━━━━━━━━━━━━━━━━━━   4.1MB                            0.3s
Extracting       ━━━━━━━━━━━━━━━━━━━━━━━       4                            1.4s
Downloading and Extracting Packages

Preparing transaction: done
Verifying transaction: done
Executing transaction: done

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        mamba (1.4.2) supported by @QuantStack

        GitHub:  https://github.com/mamba-org/mamba
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█████████████████████████████████████████████████████████████


Looking for: ['html5lib==1.1']

pkgs/main/linux-64                                          Using cache
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pkgs/r/noarch                                               Using cache

Pinned packages:
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Transaction

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   - html5lib==1.1
   - ca-certificates
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  + webencodings    0.5.1  py37_1        pkgs/main/linux-64     20kB

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  Install: 2 packages

  Total download: 113kB

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Downloading and Extracting Packages

Preparing transaction: done
Verifying transaction: done
Executing transaction: done
Collecting lxml==4.6.4
  Downloading lxml-4.6.4-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_24_x86_64.whl (6.3 MB)
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Installing collected packages: lxml
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ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
yfinance 0.2.4 requires beautifulsoup4>=4.11.1, but you have beautifulsoup4 4.10.0 which is incompatible.
yfinance 0.2.4 requires lxml>=4.9.1, but you have lxml 4.6.4 which is incompatible.
Successfully installed lxml-4.6.4
Collecting yfinance==0.1.67
  Downloading yfinance-0.1.67-py2.py3-none-any.whl (25 kB)
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Installing collected packages: yfinance
  Attempting uninstall: yfinance
    Found existing installation: yfinance 0.2.4
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        ╚═╝     ╚═╝╚═╝  ╚═╝╚═╝     ╚═╝╚═════╝ ╚═╝  ╚═╝

        mamba (1.4.2) supported by @QuantStack

        GitHub:  https://github.com/mamba-org/mamba
        Twitter: https://twitter.com/QuantStack

█████████████████████████████████████████████████████████████


Looking for: ['bs4==4.10.0']

pkgs/main/linux-64                                          Using cache
pkgs/main/noarch                                            Using cache
pkgs/r/linux-64                                             Using cache
pkgs/r/noarch                                               Using cache

Pinned packages:
  - python 3.7.*


Transaction

  Prefix: /home/jupyterlab/conda/envs/python

  All requested packages already installed

Collecting nbformat==4.2.0
  Downloading nbformat-4.2.0-py2.py3-none-any.whl (153 kB)
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Requirement already satisfied: ipython-genutils in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from nbformat==4.2.0) (0.2.0)
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Installing collected packages: nbformat
  Attempting uninstall: nbformat
    Found existing installation: nbformat 5.8.0
    Uninstalling nbformat-5.8.0:
      Successfully uninstalled nbformat-5.8.0
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
jupyter-server 1.24.0 requires nbformat>=5.2.0, but you have nbformat 4.2.0 which is incompatible.
nbclient 0.7.4 requires nbformat>=5.1, but you have nbformat 4.2.0 which is incompatible.
nbconvert 7.4.0 requires nbformat>=5.1, but you have nbformat 4.2.0 which is incompatible.
Successfully installed nbformat-4.2.0
In [4]:
import yfinance as yf
import pandas as pd
import requests
from bs4 import BeautifulSoup
import requests
from bs4 import BeautifulSoup
import plotly.graph_objects as go
from plotly.subplots import make_subplots

import warnings
warnings.filterwarnings("ignore", category=FutureWarning)

tsla = yf.Ticker("TSLA")
tsla_data = tsla.history(period="Max")
tsla_data.reset_index(inplace=True)

tsla_data.head()
Out[4]:
Date Open High Low Close Volume Dividends Stock Splits
0 2010-06-29 1.266667 1.666667 1.169333 1.592667 281494500 0 0.0
1 2010-06-30 1.719333 2.028000 1.553333 1.588667 257806500 0 0.0
2 2010-07-01 1.666667 1.728000 1.351333 1.464000 123282000 0 0.0
3 2010-07-02 1.533333 1.540000 1.247333 1.280000 77097000 0 0.0
4 2010-07-06 1.333333 1.333333 1.055333 1.074000 103003500 0 0.0
In [5]:
 
Out[5]:
Date Open High Low Close Volume Dividends Stock Splits
0 2010-06-29 1.266667 1.666667 1.169333 1.592667 281494500 0 0.0
1 2010-06-30 1.719333 2.028000 1.553333 1.588667 257806500 0 0.0
2 2010-07-01 1.666667 1.728000 1.351333 1.464000 123282000 0 0.0
3 2010-07-02 1.533333 1.540000 1.247333 1.280000 77097000 0 0.0
4 2010-07-06 1.333333 1.333333 1.055333 1.074000 103003500 0 0.0
In [6]:
tsla = yf.Ticker("TSLA")
tsla_data = tsla.history(period="Max")
tsla_data.reset_index(inplace=True)

tsla_data.tail
Out[6]:
<bound method NDFrame.tail of            Date        Open        High         Low       Close     Volume  \
0    2010-06-29    1.266667    1.666667    1.169333    1.592667  281494500   
1    2010-06-30    1.719333    2.028000    1.553333    1.588667  257806500   
2    2010-07-01    1.666667    1.728000    1.351333    1.464000  123282000   
3    2010-07-02    1.533333    1.540000    1.247333    1.280000   77097000   
4    2010-07-06    1.333333    1.333333    1.055333    1.074000  103003500   
...         ...         ...         ...         ...         ...        ...   
3456 2024-03-22  166.690002  171.199997  166.300003  170.830002   75454700   
3457 2024-03-25  168.759995  175.240005  168.729996  172.630005   74228600   
3458 2024-03-26  178.580002  184.250000  177.380005  177.669998  113186200   
3459 2024-03-27  181.410004  181.910004  176.000000  179.830002   81804000   
3460 2024-03-28  177.449997  179.570007  175.300003  175.789993   77654800   

      Dividends  Stock Splits  
0             0           0.0  
1             0           0.0  
2             0           0.0  
3             0           0.0  
4             0           0.0  
...         ...           ...  
3456          0           0.0  
3457          0           0.0  
3458          0           0.0  
3459          0           0.0  
3460          0           0.0  

[3461 rows x 8 columns]>
In [15]:
url = "https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/revenue.htm"
html_data  = requests.get(url).text
print(html_data)
<!DOCTYPE html>
<!--[if lt IE 7]>      <html class="no-js lt-ie9 lt-ie8 lt-ie7"> <![endif]-->
<!--[if IE 7]>         <html class="no-js lt-ie9 lt-ie8"> <![endif]-->
<!--[if IE 8]>         <html class="no-js lt-ie9"> <![endif]-->
<!--[if gt IE 8]><!--> <html class="no-js"> <!--<![endif]-->
    <head>
        <meta charset="utf-8">
        <meta http-equiv="X-UA-Compatible" content="IE=edge,chrome=1">
		<link rel="canonical" href="https://www.macrotrends.net/stocks/charts/TSLA/tesla/revenue" />
		<title>Tesla Revenue 2010-2022 | TSLA | MacroTrends</title>
		<meta name="description" content="Tesla annual/quarterly revenue history and growth rate from 2010 to 2022. Revenue can be defined as the amount of money a company receives from its customers in exchange for the sales of goods or services.  Revenue is the top line item on an income statement from which all costs and expenses are subtracted to arrive at net income.    
				
				&lt;ul style='margin-top:10px;'&gt;
				&lt;li&gt;Tesla revenue for the quarter ending September 30, 2022 was &lt;strong&gt;$21.454B&lt;/strong&gt;, a &lt;strong&gt;55.95% increase&lt;/strong&gt; year-over-year.&lt;/li&gt;
				&lt;li&gt;Tesla revenue for the twelve months ending September 30, 2022 was &lt;strong&gt;$74.863B&lt;/strong&gt;, a &lt;strong&gt;59.8% increase&lt;/strong&gt; year-over-year.&lt;/li&gt;
				&lt;li&gt;Tesla annual revenue for 2021 was &lt;strong&gt;$53.823B&lt;/strong&gt;, a &lt;strong&gt;70.67% increase&lt;/strong&gt; from 2020.&lt;/li&gt;
				&lt;li&gt;Tesla annual revenue for 2020 was &lt;strong&gt;$31.536B&lt;/strong&gt;, a &lt;strong&gt;28.31% increase&lt;/strong&gt; from 2019.&lt;/li&gt;
				&lt;li&gt;Tesla annual revenue for 2019 was &lt;strong&gt;$24.578B&lt;/strong&gt;, a &lt;strong&gt;14.52% increase&lt;/strong&gt; from 2018.&lt;/li&gt;
				&lt;/ul&gt;" />
		<meta name="robots" content="" />
				
		<link rel="shortcut icon" href="/assets/images/icons/FAVICON/macro-trends_favicon.ico" type="image/x-icon">

		<meta name="msvalidate.01" content="1228954C688F5907894001CD8E5E624B" />
		<meta name="google-site-verification" content="6MnD_3iDtAP1ZyoGK1YMyVIVck4r5Ws80I9xD3ue4_A" />

		<!-- Load in Roboto Font -->
		<link rel='stylesheet' href='https://fonts.googleapis.com/css?family=Roboto:400,600,700'>

		<!-- Bootstrap -->
		<link rel="stylesheet" href="//maxcdn.bootstrapcdn.com/bootstrap/3.3.7/css/bootstrap.min.css"> <!--for Bootstrap CDN version-->
		<link rel="stylesheet" href="//maxcdn.bootstrapcdn.com/bootstrap/3.3.7/css/bootstrap-theme.min.css">

		<!-- Font Awesome -->
		<link rel="stylesheet" href="//stackpath.bootstrapcdn.com/font-awesome/4.7.0/css/font-awesome.min.css"> <!--for Font Awesome CDN version-->
    		
		<!-- Jquery, Bootstrap and Menu Javascript -->	
		<script src="//code.jquery.com/jquery-1.12.4.min.js" integrity="sha256-ZosEbRLbNQzLpnKIkEdrPv7lOy9C27hHQ+Xp8a4MxAQ=" crossorigin="anonymous"></script>
		<script src="//maxcdn.bootstrapcdn.com/bootstrap/3.3.7/js/bootstrap.min.js"></script>
				
		<!-- Modernizr for cross-browser support -->		
		<script type="text/javascript" src="/assets/javascript/modernizr-2.6.2-respond-1.1.0.min.js"></script>

		<!-- Latest compiled and minified CSS -->
		<link rel="stylesheet" href="//www.fuelcdn.com/fuelux/3.13.0/css/fuelux.min.css">

		<!-- Latest compiled and minified JavaScript -->
		<script src="//www.fuelcdn.com/fuelux/3.13.0/js/fuelux.min.js"></script>
		
		
		 <!-- Twitter Card data -->
		  <meta name="twitter:card" content="summary_large_image">
		  <meta name="twitter:site" content="@tmacrotrends">
		  <meta name="twitter:title" content="Tesla Revenue 2010-2022 | TSLA">
		  <meta name="twitter:description" content="Tesla annual/quarterly revenue history and growth rate from 2010 to 2022. Revenue can be defined as the amount of money a company receives from its customers in exchange for the sales of goods or services.  Revenue is the top line item on an income statement from which all costs and expenses are subtracted to arrive at net income.    
				
				<ul style='margin-top:10px;'>
				<li>Tesla revenue for the quarter ending September 30, 2022 was <strong>$21.454B</strong>, a <strong>55.95% increase</strong> year-over-year.</li>
				<li>Tesla revenue for the twelve months ending September 30, 2022 was <strong>$74.863B</strong>, a <strong>59.8% increase</strong> year-over-year.</li>
				<li>Tesla annual revenue for 2021 was <strong>$53.823B</strong>, a <strong>70.67% increase</strong> from 2020.</li>
				<li>Tesla annual revenue for 2020 was <strong>$31.536B</strong>, a <strong>28.31% increase</strong> from 2019.</li>
				<li>Tesla annual revenue for 2019 was <strong>$24.578B</strong>, a <strong>14.52% increase</strong> from 2018.</li>
				</ul>">

		  <!-- Open Graph data -->
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				<ul style='margin-top:10px;'>
				<li>Tesla revenue for the quarter ending September 30, 2022 was <strong>$21.454B</strong>, a <strong>55.95% increase</strong> year-over-year.</li>
				<li>Tesla revenue for the twelve months ending September 30, 2022 was <strong>$74.863B</strong>, a <strong>59.8% increase</strong> year-over-year.</li>
				<li>Tesla annual revenue for 2021 was <strong>$53.823B</strong>, a <strong>70.67% increase</strong> from 2020.</li>
				<li>Tesla annual revenue for 2020 was <strong>$31.536B</strong>, a <strong>28.31% increase</strong> from 2019.</li>
				<li>Tesla annual revenue for 2019 was <strong>$24.578B</strong>, a <strong>14.52% increase</strong> from 2018.</li>
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				<h2 style="margin-left:0px; font-weight:600; color:#444;">Tesla Revenue 2010-2022 | TSLA</h2>
			
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				<span style="color:#444; line-height: 1.8;">Tesla annual/quarterly revenue history and growth rate from 2010 to 2022. Revenue can be defined as the amount of money a company receives from its customers in exchange for the sales of goods or services.  Revenue is the top line item on an income statement from which all costs and expenses are subtracted to arrive at net income.    
				
				<ul style='margin-top:10px;'>
				<li>Tesla revenue for the quarter ending September 30, 2022 was <strong>$21.454B</strong>, a <strong>55.95% increase</strong> year-over-year.</li>
				<li>Tesla revenue for the twelve months ending September 30, 2022 was <strong>$74.863B</strong>, a <strong>59.8% increase</strong> year-over-year.</li>
				<li>Tesla annual revenue for 2021 was <strong>$53.823B</strong>, a <strong>70.67% increase</strong> from 2020.</li>
				<li>Tesla annual revenue for 2020 was <strong>$31.536B</strong>, a <strong>28.31% increase</strong> from 2019.</li>
				<li>Tesla annual revenue for 2019 was <strong>$24.578B</strong>, a <strong>14.52% increase</strong> from 2018.</li>
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				<table class="historical_data_table table">
				<thead>
				  <tr>
					<th colspan=2 style="text-align:center">Tesla Annual Revenue<br /><span style="font-size:14px;">(Millions of US $)</span></th>
				  </tr>
				</thead>					
				<tbody>
								
												
				<tr>
					<td style="text-align:center">2021</td>
				
				
				
										<td style="text-align:center">$53,823</td>
				
								
				</tr>
				  
												
				<tr>
					<td style="text-align:center">2020</td>
				
				
				
										<td style="text-align:center">$31,536</td>
				
								
				</tr>
				  
												
				<tr>
					<td style="text-align:center">2019</td>
				
				
				
										<td style="text-align:center">$24,578</td>
				
								
				</tr>
				  
												
				<tr>
					<td style="text-align:center">2018</td>
				
				
				
										<td style="text-align:center">$21,461</td>
				
								
				</tr>
				  
												
				<tr>
					<td style="text-align:center">2017</td>
				
				
				
										<td style="text-align:center">$11,759</td>
				
								
				</tr>
				  
												
				<tr>
					<td style="text-align:center">2016</td>
				
				
				
										<td style="text-align:center">$7,000</td>
				
								
				</tr>
				  
												
				<tr>
					<td style="text-align:center">2015</td>
				
				
				
										<td style="text-align:center">$4,046</td>
				
								
				</tr>
				  
												
				<tr>
					<td style="text-align:center">2014</td>
				
				
				
										<td style="text-align:center">$3,198</td>
				
								
				</tr>
				  
												
				<tr>
					<td style="text-align:center">2013</td>
				
				
				
										<td style="text-align:center">$2,013</td>
				
								
				</tr>
				  
												
				<tr>
					<td style="text-align:center">2012</td>
				
				
				
										<td style="text-align:center">$413</td>
				
								
				</tr>
				  
												
				<tr>
					<td style="text-align:center">2011</td>
				
				
				
										<td style="text-align:center">$204</td>
				
								
				</tr>
				  
												
				<tr>
					<td style="text-align:center">2010</td>
				
				
				
										<td style="text-align:center">$117</td>
				
								
				</tr>
				  
												
				<tr>
					<td style="text-align:center">2009</td>
				
				
				
										<td style="text-align:center">$112</td>
				
								
				</tr>
				  
				  
				</tbody>
			  </table>
			  
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			  <div class="col-xs-6">
			
				<table class="historical_data_table table">
				<thead>
				  <tr>
					<th colspan=2 style="text-align:center">Tesla Quarterly Revenue<br /><span style="font-size:14px;">(Millions of US $)</span></th>
				  </tr>
				</thead>				
				<tbody>
				
												
				<tr>
					<td style="text-align:center">2022-09-30</td>
				
				
				
										
										
						<td style="text-align:center">$21,454</td>
						
									
								
				  </tr>
				  
												
				<tr>
					<td style="text-align:center">2022-06-30</td>
				
				
				
										
										
						<td style="text-align:center">$16,934</td>
						
									
								
				  </tr>
				  
												
				<tr>
					<td style="text-align:center">2022-03-31</td>
				
				
				
										
										
						<td style="text-align:center">$18,756</td>
						
									
								
				  </tr>
				  
												
				<tr>
					<td style="text-align:center">2021-12-31</td>
				
				
				
										
										
						<td style="text-align:center">$17,719</td>
						
									
								
				  </tr>
				  
												
				<tr>
					<td style="text-align:center">2021-09-30</td>
				
				
				
										
										
						<td style="text-align:center">$13,757</td>
						
									
								
				  </tr>
				  
												
				<tr>
					<td style="text-align:center">2021-06-30</td>
				
				
				
										
										
						<td style="text-align:center">$11,958</td>
						
									
								
				  </tr>
				  
												
				<tr>
					<td style="text-align:center">2021-03-31</td>
				
				
				
										
										
						<td style="text-align:center">$10,389</td>
						
									
								
				  </tr>
				  
												
				<tr>
					<td style="text-align:center">2020-12-31</td>
				
				
				
										
										
						<td style="text-align:center">$10,744</td>
						
									
								
				  </tr>
				  
												
				<tr>
					<td style="text-align:center">2020-09-30</td>
				
				
				
										
										
						<td style="text-align:center">$8,771</td>
						
									
								
				  </tr>
				  
												
				<tr>
					<td style="text-align:center">2020-06-30</td>
				
				
				
										
										
						<td style="text-align:center">$6,036</td>
						
									
								
				  </tr>
				  
												
				<tr>
					<td style="text-align:center">2020-03-31</td>
				
				
				
										
										
						<td style="text-align:center">$5,985</td>
						
									
								
				  </tr>
				  
												
				<tr>
					<td style="text-align:center">2019-12-31</td>
				
				
				
										
										
						<td style="text-align:center">$7,384</td>
						
									
								
				  </tr>
				  
												
				<tr>
					<td style="text-align:center">2019-09-30</td>
				
				
				
										
										
						<td style="text-align:center">$6,303</td>
						
									
								
				  </tr>
				  
												
				<tr>
					<td style="text-align:center">2019-06-30</td>
				
				
				
										
										
						<td style="text-align:center">$6,350</td>
						
									
								
				  </tr>
				  
												
				<tr>
					<td style="text-align:center">2019-03-31</td>
				
				
				
										
										
						<td style="text-align:center">$4,541</td>
						
									
								
				  </tr>
				  
												
				<tr>
					<td style="text-align:center">2018-12-31</td>
				
				
				
										
										
						<td style="text-align:center">$7,226</td>
						
									
								
				  </tr>
				  
												
				<tr>
					<td style="text-align:center">2018-09-30</td>
				
				
				
										
										
						<td style="text-align:center">$6,824</td>
						
									
								
				  </tr>
				  
												
				<tr>
					<td style="text-align:center">2018-06-30</td>
				
				
				
										
										
						<td style="text-align:center">$4,002</td>
						
									
								
				  </tr>
				  
												
				<tr>
					<td style="text-align:center">2018-03-31</td>
				
				
				
										
										
						<td style="text-align:center">$3,409</td>
						
									
								
				  </tr>
				  
												
				<tr>
					<td style="text-align:center">2017-12-31</td>
				
				
				
										
										
						<td style="text-align:center">$3,288</td>
						
									
								
				  </tr>
				  
												
				<tr>
					<td style="text-align:center">2017-09-30</td>
				
				
				
										
										
						<td style="text-align:center">$2,985</td>
						
									
								
				  </tr>
				  
												
				<tr>
					<td style="text-align:center">2017-06-30</td>
				
				
				
										
										
						<td style="text-align:center">$2,790</td>
						
									
								
				  </tr>
				  
												
				<tr>
					<td style="text-align:center">2017-03-31</td>
				
				
				
										
										
						<td style="text-align:center">$2,696</td>
						
									
								
				  </tr>
				  
												
				<tr>
					<td style="text-align:center">2016-12-31</td>
				
				
				
										
										
						<td style="text-align:center">$2,285</td>
						
									
								
				  </tr>
				  
												
				<tr>
					<td style="text-align:center">2016-09-30</td>
				
				
				
										
										
						<td style="text-align:center">$2,298</td>
						
									
								
				  </tr>
				  
												
				<tr>
					<td style="text-align:center">2016-06-30</td>
				
				
				
										
										
						<td style="text-align:center">$1,270</td>
						
									
								
				  </tr>
				  
												
				<tr>
					<td style="text-align:center">2016-03-31</td>
				
				
				
										
										
						<td style="text-align:center">$1,147</td>
						
									
								
				  </tr>
				  
												
				<tr>
					<td style="text-align:center">2015-12-31</td>
				
				
				
										
										
						<td style="text-align:center">$1,214</td>
						
									
								
				  </tr>
				  
												
				<tr>
					<td style="text-align:center">2015-09-30</td>
				
				
				
										
										
						<td style="text-align:center">$937</td>
						
									
								
				  </tr>
				  
												
				<tr>
					<td style="text-align:center">2015-06-30</td>
				
				
				
										
										
						<td style="text-align:center">$955</td>
						
									
								
				  </tr>
				  
												
				<tr>
					<td style="text-align:center">2015-03-31</td>
				
				
				
										
										
						<td style="text-align:center">$940</td>
						
									
								
				  </tr>
				  
												
				<tr>
					<td style="text-align:center">2014-12-31</td>
				
				
				
										
										
						<td style="text-align:center">$957</td>
						
									
								
				  </tr>
				  
												
				<tr>
					<td style="text-align:center">2014-09-30</td>
				
				
				
										
										
						<td style="text-align:center">$852</td>
						
									
								
				  </tr>
				  
												
				<tr>
					<td style="text-align:center">2014-06-30</td>
				
				
				
										
										
						<td style="text-align:center">$769</td>
						
									
								
				  </tr>
				  
												
				<tr>
					<td style="text-align:center">2014-03-31</td>
				
				
				
										
										
						<td style="text-align:center">$621</td>
						
									
								
				  </tr>
				  
												
				<tr>
					<td style="text-align:center">2013-12-31</td>
				
				
				
										
										
						<td style="text-align:center">$615</td>
						
									
								
				  </tr>
				  
												
				<tr>
					<td style="text-align:center">2013-09-30</td>
				
				
				
										
										
						<td style="text-align:center">$431</td>
						
									
								
				  </tr>
				  
												
				<tr>
					<td style="text-align:center">2013-06-30</td>
				
				
				
										
										
						<td style="text-align:center">$405</td>
						
									
								
				  </tr>
				  
												
				<tr>
					<td style="text-align:center">2013-03-31</td>
				
				
				
										
										
						<td style="text-align:center">$562</td>
						
									
								
				  </tr>
				  
												
				<tr>
					<td style="text-align:center">2012-12-31</td>
				
				
				
										
										
						<td style="text-align:center">$306</td>
						
									
								
				  </tr>
				  
												
				<tr>
					<td style="text-align:center">2012-09-30</td>
				
				
				
										
										
						<td style="text-align:center">$50</td>
						
									
								
				  </tr>
				  
												
				<tr>
					<td style="text-align:center">2012-06-30</td>
				
				
				
										
										
						<td style="text-align:center">$27</td>
						
									
								
				  </tr>
				  
												
				<tr>
					<td style="text-align:center">2012-03-31</td>
				
				
				
										
										
						<td style="text-align:center">$30</td>
						
									
								
				  </tr>
				  
												
				<tr>
					<td style="text-align:center">2011-12-31</td>
				
				
				
										
										
						<td style="text-align:center">$39</td>
						
									
								
				  </tr>
				  
												
				<tr>
					<td style="text-align:center">2011-09-30</td>
				
				
				
										
										
						<td style="text-align:center">$58</td>
						
									
								
				  </tr>
				  
												
				<tr>
					<td style="text-align:center">2011-06-30</td>
				
				
				
										
										
						<td style="text-align:center">$58</td>
						
									
								
				  </tr>
				  
												
				<tr>
					<td style="text-align:center">2011-03-31</td>
				
				
				
										
										
						<td style="text-align:center">$49</td>
						
									
								
				  </tr>
				  
												
				<tr>
					<td style="text-align:center">2010-12-31</td>
				
				
				
										
										
						<td style="text-align:center">$36</td>
						
									
								
				  </tr>
				  
												
				<tr>
					<td style="text-align:center">2010-09-30</td>
				
				
				
										
										
						<td style="text-align:center">$31</td>
						
									
								
				  </tr>
				  
												
				<tr>
					<td style="text-align:center">2010-06-30</td>
				
				
				
										
										
						<td style="text-align:center">$28</td>
						
									
								
				  </tr>
				  
												
				<tr>
					<td style="text-align:center">2010-03-31</td>
				
				
				
										
										
						<td style="text-align:center">$21</td>
						
									
								
				  </tr>
				  
												
				<tr>
					<td style="text-align:center">2009-12-31</td>
				
				
				
										
					
						<td style="text-align:center"></td>					
					
									
								
				  </tr>
				  
												
				<tr>
					<td style="text-align:center">2009-09-30</td>
				
				
				
										
										
						<td style="text-align:center">$46</td>
						
									
								
				  </tr>
				  
												
				<tr>
					<td style="text-align:center">2009-06-30</td>
				
				
				
										
										
						<td style="text-align:center">$27</td>
						
									
								
				  </tr>
				  
				  
				</tbody>
			  </table>
			  
			  </div>
			  

			
</div>			
			
			
		
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			  <table class="historical_data_table table">
				<thead>
				  <tr>
					<th style="text-align:center">Sector</th>
					<th style="text-align:center">Industry</th>
					<th style="text-align:center">Market Cap</th>
					<th style="text-align:center">Revenue</th>
				  </tr>
				</thead>
				<tbody>
				  <tr>
					<td style="text-align:center"><a href='https://www.macrotrends.net/stocks/sector/5/auto-tires-trucks'>Auto/Tires/Trucks</a></td>
					<td style="text-align:center"><a href='https://www.macrotrends.net/stocks/industry/7/'>Auto Manufacturers - Domestic</a></td>
					<td style="text-align:center">$549.575B</td>
					<td style="text-align:center">$53.823B</td>
				  </tr>
				  <tr>
					  <td colspan="4" style="padding:15px;">
						<span>Tesla is the market leader in battery-powered electric car sales in the United States, with roughly 70% market share. The company's flagship Model 3 is the best-selling EV model in the United States. Tesla, which has managed to garner the reputation of a gold standard over the years, is now a far bigger entity that what it started off since its IPO in 2010, with its market cap crossing $1 trillion for the first time in October 2021.? The EV king's market capitalization is more than the combined value of legacy automakers including Toyota, Volkswagen, Daimler, General Motors and Ford.Over the years, Tesla has shifted from developing niche products for affluent buyers to making more affordable EVs for the masses. The firm's three-pronged business model approach of direct sales, servicing, and charging its EVs sets it apart from other carmakers. Tesla, which is touted as the clean energy revolutionary automaker, is much more than just a car manufacturer.</span>
					  </td>
				  </tr>
				</tbody>
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				<table class="historical_data_table table">
				<thead>
				  <tr>
					<th style="text-align:center; width:40%;">Stock Name</th>
					<th style="text-align:center; width:20%;">Country</th>
					<th style="text-align:center; width:20%;">Market Cap</th>
					<th style="text-align:center; width:20%;">PE Ratio</th>
				  </tr>
				</thead>
				<tbody>			

						
						
				<tr>
					<td style="text-align:left"><a href='/stocks/charts/GM/general-motors/revenue'>General Motors (GM)</a></td>
					<td style="text-align:center">United States</td>
					<td style="text-align:center">$53.930B</td>
					<td style="text-align:center">5.56</td>
				 </tr>

						
						
				<tr>
					<td style="text-align:left"><a href='/stocks/charts/F/ford-motor/revenue'>Ford Motor (F)</a></td>
					<td style="text-align:center">United States</td>
					<td style="text-align:center">$52.668B</td>
					<td style="text-align:center">8.09</td>
				 </tr>

						
						
				<tr>
					<td style="text-align:left"><a href='/stocks/charts/HOG/harley-davidson/revenue'>Harley-Davidson (HOG)</a></td>
					<td style="text-align:center">United States</td>
					<td style="text-align:center">$6.762B</td>
					<td style="text-align:center">9.56</td>
				 </tr>

						
						
				<tr>
					<td style="text-align:left"><a href='/stocks/charts/PII/polaris/revenue'>Polaris (PII)</a></td>
					<td style="text-align:center">United States</td>
					<td style="text-align:center">$6.267B</td>
					<td style="text-align:center">11.86</td>
				 </tr>

						
						
				<tr>
					<td style="text-align:left"><a href='/stocks/charts/IAA/iaa/revenue'>IAA (IAA)</a></td>
					<td style="text-align:center">United States</td>
					<td style="text-align:center">$5.134B</td>
					<td style="text-align:center">16.40</td>
				 </tr>

						
						
				<tr>
					<td style="text-align:left"><a href='/stocks/charts/FSR/fisker/revenue'>Fisker (FSR)</a></td>
					<td style="text-align:center">United States</td>
					<td style="text-align:center">$2.261B</td>
					<td style="text-align:center">0.00</td>
				 </tr>

						
						
				<tr>
					<td style="text-align:left"><a href='/stocks/charts/LEV/lion-electric/revenue'>Lion Electric (LEV)</a></td>
					<td style="text-align:center">Canada</td>
					<td style="text-align:center">$0.551B</td>
					<td style="text-align:center">0.00</td>
				 </tr>

						
						
				<tr>
					<td style="text-align:left"><a href='/stocks/charts/VLTA/volta/revenue'>Volta (VLTA)</a></td>
					<td style="text-align:center">United States</td>
					<td style="text-align:center">$0.071B</td>
					<td style="text-align:center">0.00</td>
				 </tr>

						
						
				<tr>
					<td style="text-align:left"><a href='/stocks/charts/BRDS/bird-global/revenue'>Bird Global (BRDS)</a></td>
					<td style="text-align:center">United States</td>
					<td style="text-align:center">$0.054B</td>
					<td style="text-align:center">0.00</td>
				 </tr>

						
						
				<tr>
					<td style="text-align:left"><a href='/stocks/charts/ZEV/lightning-emotors/revenue'>Lightning EMotors (ZEV)</a></td>
					<td style="text-align:center">United States</td>
					<td style="text-align:center">$0.043B</td>
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In [24]:
url = "https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/revenue.htm"
html_data  = requests.get(url).text

soup = BeautifulSoup(html_data, 'html5lib')

tsla_revenue = pd.DataFrame(columns=["Revenue"])

for row in soup.find_all("tbody")[1]:
    Revenue = col[1].text

    tsla_revenue = tsla_revenue.append({"Revenue":Revenue}, ignore_index=True)
    
tsla_revenue["Revenue"] = tsla_revenue['Revenue'].str.replace(',|\$',"")

tsla_revenue.dropna(inplace=True)

tsla_revenue = tsla_revenue[tsla_revenue['Revenue'] != ""]

tsla_revenue.tail()
Out[24]:
Revenue
104 112
105 112
106 112
107 112
108 112
In [26]:
import yfinance as yf
import pandas as pd
import requests
from bs4 import BeautifulSoup
import requests
from bs4 import BeautifulSoup
import plotly.graph_objects as go
from plotly.subplots import make_subplots

import warnings
warnings.filterwarnings("ignore", category=FutureWarning)

gme = yf.Ticker("GME")
gme_data = gme.history(period="Max")
gme_data.reset_index(inplace=True)

gme_data.head()
Out[26]:
Date Open High Low Close Volume Dividends Stock Splits
0 2002-02-13 1.620128 1.693350 1.603296 1.691666 76216000 0.0 0.0
1 2002-02-14 1.712708 1.716074 1.670626 1.683251 11021600 0.0 0.0
2 2002-02-15 1.683251 1.687459 1.658002 1.674834 8389600 0.0 0.0
3 2002-02-19 1.666417 1.666417 1.578047 1.607504 7410400 0.0 0.0
4 2002-02-20 1.615921 1.662210 1.603296 1.662210 6892800 0.0 0.0
In [27]:
url = "https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/stock.html"
html_data  = requests.get(url).text

soup = BeautifulSoup(html_data, 'html5lib')

gme_revenue = pd.DataFrame(columns=["Revenue"])

for row in soup.find_all("tbody")[1]:
    Revenue = col[1].text

    gme_revenue = gme_revenue.append({"Revenue":Revenue}, ignore_index=True)
    
gme_revenue["Revenue"] = gme_revenue['Revenue'].str.replace(',|\$',"")

gme_revenue.dropna(inplace=True)

gme_revenue = gme_revenue[gme_revenue['Revenue'] != ""]

gme_revenue.tail()
Out[27]:
Revenue
120 112
121 112
122 112
123 112
124 112
In [31]:
import yfinance as yf
import pandas as pd
import requests
from bs4 import BeautifulSoup
import requests
from bs4 import BeautifulSoup
import plotly.graph_objects as go
from plotly.subplots import make_subplots

import warnings
warnings.filterwarnings("ignore", category=FutureWarning)

def make_graph(stock_data, revenue_data, stock):
    fig = make_subplots(rows=2, cols=1, shared_xaxes=True, subplot_titles=("Historical Share Price", "Historical Revenue"), vertical_spacing = .3)
    stock_data_specific = stock_data[stock_data.Date <= '2021--06-14']
    revenue_data_specific = revenue_data[revenue_data.Date <= '2021-04-30']
    fig.add_trace(go.Scatter(x=pd.to_datetime(stock_data_specific.Date, infer_datetime_format=True), y=stock_data_specific.Close.astype("float"), name="Share Price"), row=1, col=1)
    fig.add_trace(go.Scatter(x=pd.to_datetime(revenue_data_specific.Date, infer_datetime_format=True), y=revenue_data_specific.Revenue.astype("float"), name="Revenue"), row=2, col=1)
    fig.update_xaxes(title_text="Date", row=1, col=1)
    fig.update_xaxes(title_text="Date", row=2, col=1)
    fig.update_yaxes(title_text="Price ($US)", row=1, col=1)
    fig.update_yaxes(title_text="Revenue ($US Millions)", row=2, col=1)
    fig.update_layout(showlegend=False,
    height=900,
    title=stock,
    xaxis_rangeslider_visible=True)
    fig.show()
tsla = yf.Ticker("TSLA")
tsla_data = tsla.history(period="Max")
tsla_data.reset_index(inplace=True)

tsla_data.head()

url = "https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/revenue.htm"
html_data  = requests.get(url).text

soup = BeautifulSoup(html_data, 'html5lib')

tsla_revenue = pd.DataFrame(columns=["Date","Revenue"])

for row in soup.find_all("tbody")[1]:
    Date = col[0].text
    Revenue = col[1].text

    tsla_revenue = tsla_revenue.append({"Date":Date,"Revenue":Revenue}, ignore_index=True)
    
tsla_revenue["Revenue"] = tsla_revenue['Revenue'].str.replace(',|\$',"")

tsla_revenue.dropna(inplace=True)

tsla_revenue = tsla_revenue[tsla_revenue['Revenue'] != ""]

tsla_revenue.tail()

make_graph(tsla_data, tsla_revenue, 'Tesla')
In [35]:
import yfinance as yf
import pandas as pd
import requests
from bs4 import BeautifulSoup
import requests
from bs4 import BeautifulSoup
import plotly.graph_objects as go
from plotly.subplots import make_subplots

import warnings
warnings.filterwarnings("ignore", category=FutureWarning)

def make_graph(stock_data, revenue_data, stock):
    fig = make_subplots(rows=2, cols=1, shared_xaxes=True, subplot_titles=("Historical Share Price", "Historical Revenue"), vertical_spacing = .3)
    stock_data_specific = stock_data[stock_data.Date <= '2021--06-14']
    revenue_data_specific = revenue_data[revenue_data.Date <= '2021-04-30']
    fig.add_trace(go.Scatter(x=pd.to_datetime(stock_data_specific.Date, infer_datetime_format=True), y=stock_data_specific.Close.astype("float"), name="Share Price"), row=1, col=1)
    fig.add_trace(go.Scatter(x=pd.to_datetime(revenue_data_specific.Date, infer_datetime_format=True), y=revenue_data_specific.Revenue.astype("float"), name="Revenue"), row=2, col=1)
    fig.update_xaxes(title_text="Date", row=1, col=1)
    fig.update_xaxes(title_text="Date", row=2, col=1)
    fig.update_yaxes(title_text="Price ($US)", row=1, col=1)
    fig.update_yaxes(title_text="Revenue ($US Millions)", row=2, col=1)
    fig.update_layout(showlegend=False,
    height=900,
    title=stock,
    xaxis_rangeslider_visible=True)
    fig.show()
gme = yf.Ticker("GME")
gme_data = gme.history(period="Max")
gme_data.reset_index(inplace=True)

gme_data.head()

url = "https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/stock.html"
html_data  = requests.get(url).text

soup = BeautifulSoup(html_data, 'html5lib')

gme_revenue = pd.DataFrame(columns=["Date","Revenue"])

for row in soup.find_all("tbody")[1]:
    Date = col[0].text
    Revenue = col[1].text

    gme_revenue = gme_revenue.append({"Date":Date,"Revenue":Revenue}, ignore_index=True)
    
gme_revenue["Revenue"] = gme_revenue['Revenue'].str.replace(',|\$',"")

gme_revenue.dropna(inplace=True)

gme_revenue = gme_revenue[gme_revenue['Revenue'] != ""]

gme_revenue.tail()

make_graph(gme_data, gme_revenue, 'GameStop')
In [ ]: